STT465: Bayesian Statistical Methods (MSU) Instructor: Gustavo de los Campos ( gustavoc@msu.edu ) Time/Place: MW 10:20am-11:40am A120 Wells Hall (WH) Syllabus Required textbook R-software In this course we follow closely the required textbook: "A first Course in Bayesian Statistical Methods" (P.D. Hoff). HW Homework 1 Homework 2 Gout data Homework 3 Homework 4 Lectures Chapter 1: Introduction and examples Lecture Chapter 2: Belief, probability and exchangeability Lecture Chapter 3: One-parameter models Lecture Chapter 4: Monte Carlo approximations Lecture Examples Chapter 5: The normal model Lecture Examples Chapter 6: Posterior approximation with the Gibbs sampler Lecture Examples Review of Linear Algebra & Multivariate Normal Lecture Multiple linear regression OLS and Maximum Likelihood Lecture Notes The multivariate normal distribution & intro to Bayesian multiple linear regression Slides set 1 Slides set 2 Chapter 10: Nonconjugate priors and Metropolis-Hastings algorithms Lecture Examples Chapter 11: Linear and generalized linear mixed effects models Lecture Examples Chapter 12: Latent variable methods for ordinal data Lecture Examples